bidvertiser01

Saturday, September 15, 2012

Describe Pattern Recognition problem?

Methods for solving pattern
recognition tasks generally assume a sequential model for the pattern
recognition process, consisting of pattern environment, sensors to collect data
from the environment, feature extraction from the data and association/
storage/classification/clustering using the features. The simplest solution to
a pattern recognition problem is to use template matching, where the data of
the test pattern are matched point by point with the corresponding data in the
reference pattern. Obviously, this can work only for very simple and highly
restricted pattern recognition tasks. At the next level of complexity, one can
assume a deterministic model for the pattern generation process, and derive the
parameters of the model from given data in order to represent the pattern
information in the data. Matching test and reference patterns are done at the
parametric level. This works well when the model of the gene;ation process is
known with reasonable accuracy. One could also assume a stochastic model for
the pattern generation process, and derive the parameters of the model from a
large set of training patterns. Matching between test and reference patterns
can be performed by several statistical methods like likelihood ratio, variance
weighted distance, Bayesian classification etc. Other approaches for pattern
recognition tasks depend on extracting features from parameters or data. These
features may be specific for the task. A pattern is described in terms of
features, and pattern matching is done using descriptions of the features.

Another method based on
descriptions is called syntactic or structural pattern recognition in which a
pattern in expressed in terms of primitives suitable for the classes of pattern
under study (Schalkoft 1992). Pattern matching is performed by matching the
descriptions of the patterns in terms of the primitives. More recently, methods
based on the knowledge of the sources generating the patterns are being
explored for pattern recognition tasks. These knowledge-based systems express I
knowledge in the form of rules for generating and perceiving patterns.

The main difficulty in each of
the pattern recognition techniques alluded to above is that of choosing an
appropriate model for the pattern generating process and estimating the
parameters of the model in the case of a model-based approach, or extraction of
features from data parameters in the case of feature-based methods, or
selecting appropriate primitives in the case of syntactic pattern recognition,
or deriving rules in the case of a knowledge-based approach. It is all the more
difficult when the test patterns are noisy and distorted versions of the
patterns used in the training process. The ultimate goal is to impart to a
machine the pattern recognition capabilities comparable to those of human
beings. This goal is difficult to achieve using most of the conventional
methods, because, as mentioned earlier, these methods assume a sequential model
for the pattern recognition process. On the other hand, the human pattern
recognition process is an integrated process involving the use of biological
neural processing even from the stage of sensing the environment. Thus the
neural processing takes place directly on the data for feature extraction and
pattern matching. Moreover, the large size (in terms of number of neurons and
interconnections) of the biological neural network and the inherently different
r mechanism of processing are attributed to our abilities of pattern
recognition in spite of variability and noise in the data. Moreover, we are
able to deal effortlessly with temporal patterns and also with the so-called
stability-plasticity dilemma as well.

It
is for these reasons attempts are being made to explore new models of
computing, inspired by the structure and function of the biological neural
network. Such models for computing are based on artificial neural networks,